Prediction of students’ performance is one of the most explored issues in educational data mining. To predict if students will achieve the outcomes of the subject based on the previous results enables teachers to adapt the learning design of the subject to the teaching-learning process. However, this adaptation is even more relevant if we could predict the fulfillment of the educational objectives of a subject, since teachers should focus the adaptation on the learning resources and activities related to those educational objectives. In this paper, we present an experiment where a support vector machine is applied as a classifier that predicts if the different educational objectives of a subject are achieved or not. The inputs of the problem are the marks obtained by the students in the questionnaires related to the learning activities that students must undertake during the course. The results are very good, since the classifiers predict the achievement of the educational objectives with precision over 80%.